Share and discover more about AI with social posts from the community.huggingface/OpenAi
🔥 Leading Performance. MiniCPM-V 2.6 achieves an average score of 65.2 on the latest version of OpenCompass, a comprehensive evaluation over 8 popular benchmarks. With only 8B parameters, it surpasses widely used proprietary models like GPT-4o mini, GPT-4V, Gemini 1.5 Pro, and Claude 3.5 Sonnet for single image understanding.
Level MLLM for Single Image, Multi Image and Video on Your Phone
GitHub | Demo
MiniCPM-V 2.6
MiniCPM-V 2.6 is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-Llama3-V 2.5, and introduces new features for multi-image and video understanding. Notable features of MiniCPM-V 2.6 include:
GitHub | Demo
MiniCPM-V 2.6
MiniCPM-V 2.6 is the latest and most capable model in the MiniCPM-V series. The model is built on SigLip-400M and Qwen2-7B with a total of 8B parameters. It exhibits a significant performance improvement over MiniCPM-Llama3-V 2.5, and introduces new features for multi-image and video understanding. Notable features of MiniCPM-V 2.6 include:
New SmolLM-1.7B-Instruct
SmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters.
These models are pre-trained on SmolLM-Corpus, a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our blogpost.
To build SmolLM-Instruct, we finetuned the base models on publicly available datasets.
https://huggingface.co/HuggingFaceTB/SmolLM-1.7B-Instruct-v0.2
SmolLM is a series of small language models available in three sizes: 135M, 360M, and 1.7B parameters.
These models are pre-trained on SmolLM-Corpus, a curated collection of high-quality educational and synthetic data designed for training LLMs. For further details, we refer to our blogpost.
To build SmolLM-Instruct, we finetuned the base models on publicly available datasets.
https://huggingface.co/HuggingFaceTB/SmolLM-1.7B-Instruct-v0.2
1/4 Reproducing research results in ML is hard: no code, vague descriptions, noisy results.A lot of effort
@huggingface
goes into making new methods available for the community, thus we wrote a blog with the challenges and strategies on the example of
@GoogleAI
’s Infini-Attention
2/4 We attempted to reproduce Infini-Attention and found it generates content related to earlier segments, but it isn’t good enough to recall the needle in the haystack. We also faced convergence issues and wanted to share how we debugged them.
Link: http://huggingface.co/blog/infini-attention
@huggingface
goes into making new methods available for the community, thus we wrote a blog with the challenges and strategies on the example of
@GoogleAI
’s Infini-Attention
2/4 We attempted to reproduce Infini-Attention and found it generates content related to earlier segments, but it isn’t good enough to recall the needle in the haystack. We also faced convergence issues and wanted to share how we debugged them.
Link: http://huggingface.co/blog/infini-attention
SmolLM Instruct v0.2 - 135M, 360M and 1.7B parameter instruction tuned Small LMs, Apache 2.0 licensed. Closing the gap to bring intelligence closer to thought (<500 ms per generation)! 🔥
The models are optimised to run on-device with WebGPU support (from MLC and ONNX Runtime) and llama.cpp.
Run them on your Mac, browser, GPU, CPU - it works blazingly fast.
We provide already converted/ quantised - GGUFs, MLC and ONNX checkpoints 🐐
What's new?
We train SmolLM base models on a new synthetic dataset of 2,000 simple everyday conversations we generated by llama3.1-70B -> everyday-conversations-llama3.1-2k
and existing datasets like Magpie-Pro-300K-Filtered by
@argilla_io
, self-oss-instruct-sc2-exec-filter-50k, and a small subset of OpenHermes-2.5 from
@NousResearch
Bonus: We release the fine-tuning scripts we used to train these checkpoints, so that you can fine-tune them for your own use-cases too. ⚡️
Enjoy! and looking forward to what you build with these https://huggingface.co/collections/HuggingFaceTB/local-smollms-66c0f3b2a15b4eed7fb198d0
The models are optimised to run on-device with WebGPU support (from MLC and ONNX Runtime) and llama.cpp.
Run them on your Mac, browser, GPU, CPU - it works blazingly fast.
We provide already converted/ quantised - GGUFs, MLC and ONNX checkpoints 🐐
What's new?
We train SmolLM base models on a new synthetic dataset of 2,000 simple everyday conversations we generated by llama3.1-70B -> everyday-conversations-llama3.1-2k
and existing datasets like Magpie-Pro-300K-Filtered by
@argilla_io
, self-oss-instruct-sc2-exec-filter-50k, and a small subset of OpenHermes-2.5 from
@NousResearch
Bonus: We release the fine-tuning scripts we used to train these checkpoints, so that you can fine-tune them for your own use-cases too. ⚡️
Enjoy! and looking forward to what you build with these https://huggingface.co/collections/HuggingFaceTB/local-smollms-66c0f3b2a15b4eed7fb198d0
huggingface.co
💻 Local SmolLMs - a HuggingFaceTB Collection
SmolLM models in MLC, ONNX and GGUF format for local applications + in-browser demos
How good are you at spotting AI-generated images?
Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments!
victor/fake-insectshttps://cdn-uploads.huggingface.co/production/uploads/5f17f0a0925b9863e28ad517/Gn3CqEf83euvbSF1089W5.png
Find out by playing Fake Insects 🐞 a Game where you need to identify which insects are fake (AI generated). Good luck & share your best score in the comments!
victor/fake-insectshttps://cdn-uploads.huggingface.co/production/uploads/5f17f0a0925b9863e28ad517/Gn3CqEf83euvbSF1089W5.png
Brief backstory: Before diving into AI, I spent over a decade working in ecological fields such as the conservation corps, biodynamic farming, and natural habitat restoration. This background instilled in me a deep concern about the environmental impact of scaling AI without sustainable practices.
Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!
My current setup includes multiple linked Mac Pro tower desktops and custom code built from open-source libraries. While it's a bit experimental, this configuration is working great for my needs. All my LLM research, development, and client services now run exclusively on solar energy.
I'm curious if anyone else here has experimented with renewable energy for their LLM work?
For those interested in more details, I've written a brief blog post about this journey here https://medium.com/@betalabsllm/powering-the-future-be-ta-labs-revolutionary-100-solar-powered-ai-operation-444433e61d43
Driven by this concern, I've spent months planning and experimenting to make my AI work more eco-friendly. I'm thrilled to announce that I've successfully transitioned my entire operation to run on 100% sustainable solar power!
My current setup includes multiple linked Mac Pro tower desktops and custom code built from open-source libraries. While it's a bit experimental, this configuration is working great for my needs. All my LLM research, development, and client services now run exclusively on solar energy.
I'm curious if anyone else here has experimented with renewable energy for their LLM work?
For those interested in more details, I've written a brief blog post about this journey here https://medium.com/@betalabsllm/powering-the-future-be-ta-labs-revolutionary-100-solar-powered-ai-operation-444433e61d43
Medium
Powering the Future: Be.Ta Labs’ Revolutionary 100% Solar-Powered AI Operation
In today’s rapidly advancing tech world, the environmental cost of AI is becoming increasingly concerning. One company is rewriting the…
Ghost 8B Beta (1608), a top-performing language model with unmatched multilingual support and cost efficiency.
Key Highlights:
- Superior Performance: Outperforms Llama 3.1 8B Instruct, GPT-3.5 Turbo, Claude 3 Opus, GPT-4, and more in winrate scores.
- Expanded Language Support: Now supports 16 languages, including English, Vietnamese, Spanish, Chinese, and more.
- Enhanced Capabilities: Improved math, reasoning, and instruction-following for better task handling.
With two context options (8k and 128k), Ghost 8B Beta is perfect for complex, multilingual applications, balancing power and cost-effectiveness.
🔗 Learn More: https://ghost-x.org/docs/models/ghost-8b-beta
ghost-x/ghost-8b-beta-668ead6179f93be717db4542
Key Highlights:
- Superior Performance: Outperforms Llama 3.1 8B Instruct, GPT-3.5 Turbo, Claude 3 Opus, GPT-4, and more in winrate scores.
- Expanded Language Support: Now supports 16 languages, including English, Vietnamese, Spanish, Chinese, and more.
- Enhanced Capabilities: Improved math, reasoning, and instruction-following for better task handling.
With two context options (8k and 128k), Ghost 8B Beta is perfect for complex, multilingual applications, balancing power and cost-effectiveness.
🔗 Learn More: https://ghost-x.org/docs/models/ghost-8b-beta
ghost-x/ghost-8b-beta-668ead6179f93be717db4542
Ghost X
Ghost 8B Beta
A large language model was developed with goals including excellent multilingual support, superior knowledge capabilities and cost efficiency.
Bensbites
AI won’t take your job,
when you know how to use it.
Simple, bite-sized education designed to boost your AI knowledge—from beginner to pro.
Join 100k+ subscribers staying up to date on AI with our daily digest.
https://bensbites.com/?via=aidevin
AI won’t take your job,
when you know how to use it.
Simple, bite-sized education designed to boost your AI knowledge—from beginner to pro.
Join 100k+ subscribers staying up to date on AI with our daily digest.
https://bensbites.com/?via=aidevin
Bensbites
AI made simple - learn how to use AI for work | Ben's Bites
Learn how to harness the power of AI to boost your productivity at work. Our tutorials show you how to leverage AI for content creation, data analysis, automation and more. Upskill with the latest AI tools and techniques.
I made my first Flux Lora style on Civitai : r/StableDiffusion
New Makima flux lora | image created by WhiteZ | Tensor.Art
flux_makima, woman, collared shirt, white shirt, black necktie, black pants, red hair, single braid, in the office holding a sign with the text: "tensor art", smiling evilly , pixiv
flux_makima, woman, collared shirt, white shirt, black necktie, black pants, red hair, single braid, in the office holding a sign with the text: "tensor art", smiling evilly , pixiv
New Flux Lora - Flat Colour Anime v3 : r/StableDiffusion
Hi all. I just released a new version of my Flat Color Anime lora updated for Flux Dev.
https://civitai.com/models/180891/flat-color-anime?modelVersionId=724241
This was something I worked on for SDXL a while ago to try and get a clean anime style similar to some of the 1.5 checkpoints I liked. I got the inspiration with Flux to have a go at updating it and I'm loving how the results are coming out so thought I'd share. I'll be refining it over the next days/weeks so any feedback is appreciated. Enjoy! https://www.reddit.com/r/StableDiffusion/comments/1esrzf1/new_flux_lora_flat_colour_anime_v3/https://preview.redd.it/new-flux-lora-flat-colour-anime-v3-v0-mm7fsj1c4tid1.png?width=832&format=png&auto=webp&s=7022ef6a073cfa9a7367f9ca47c7ca64cec72424
Hi all. I just released a new version of my Flat Color Anime lora updated for Flux Dev.
https://civitai.com/models/180891/flat-color-anime?modelVersionId=724241
This was something I worked on for SDXL a while ago to try and get a clean anime style similar to some of the 1.5 checkpoints I liked. I got the inspiration with Flux to have a go at updating it and I'm loving how the results are coming out so thought I'd share. I'll be refining it over the next days/weeks so any feedback is appreciated. Enjoy! https://www.reddit.com/r/StableDiffusion/comments/1esrzf1/new_flux_lora_flat_colour_anime_v3/https://preview.redd.it/new-flux-lora-flat-colour-anime-v3-v0-mm7fsj1c4tid1.png?width=832&format=png&auto=webp&s=7022ef6a073cfa9a7367f9ca47c7ca64cec72424
Civitai
Flat Color Anime v3.1 - v3.1 Flux Dev | Stable Diffusion LoRA | Civitai
Anime Flat Color for Flux Dev Update 3.1! Trained using a brand new high quality dataset captioned using natural language for Flux Dev. This is an ...
Online FLUX! fal.ai integrates ControlNet, providing online LoRA training
Exciting news for drawing enthusiasts! "FLUX Online Edition" at fal.ai has introduced a series of powerful new features, including important modules like ControlNet and LoRA. The best part? These features are ready to use straight out of the box, without the need for complex configurations.
Feature Highlights
ControlNet: This new feature makes it effortless to modify character expressions in images and even control objects within the scene.
LoRA Model Online Training: Users can train their own LoRA models online and share them via links, allowing creativity to spread widely.
The robust capabilities of FLUX, combined with third-party developed ControlNet and LoRA, make drawing more flexible and personalized. Netizens have already started creating a variety of styles with these tools, from cyberpunk to Chinese traditional, limited only by imagination.
The ease of use of FLUX Online Edition is one of the key reasons for its popularity. Users do not need to deploy models or set up workflows themselves; they can simply use these advanced drawing tools through the fal.ai platform.
fal.ai is one of the official online inference platforms cooperated with the FLUX development team, Black Forest Lab. Team member Jonathan Fischoff actively shares works created by users on the platform on Twitter, showcasing the diverse applications of FLUX and LoRA models.
Netizens have reacted enthusiastically to the new features of FLUX Online Edition, looking forward to combining ControlNet and LoRA with image-to-image functions to create more diverse visual effects.
Fischoff demonstrated images generated by FLUX using ControlNet contour control, and how to quickly generate new images with different styles by modifying prompt words.
Exciting news for drawing enthusiasts! "FLUX Online Edition" at fal.ai has introduced a series of powerful new features, including important modules like ControlNet and LoRA. The best part? These features are ready to use straight out of the box, without the need for complex configurations.
Feature Highlights
ControlNet: This new feature makes it effortless to modify character expressions in images and even control objects within the scene.
LoRA Model Online Training: Users can train their own LoRA models online and share them via links, allowing creativity to spread widely.
The robust capabilities of FLUX, combined with third-party developed ControlNet and LoRA, make drawing more flexible and personalized. Netizens have already started creating a variety of styles with these tools, from cyberpunk to Chinese traditional, limited only by imagination.
The ease of use of FLUX Online Edition is one of the key reasons for its popularity. Users do not need to deploy models or set up workflows themselves; they can simply use these advanced drawing tools through the fal.ai platform.
fal.ai is one of the official online inference platforms cooperated with the FLUX development team, Black Forest Lab. Team member Jonathan Fischoff actively shares works created by users on the platform on Twitter, showcasing the diverse applications of FLUX and LoRA models.
Netizens have reacted enthusiastically to the new features of FLUX Online Edition, looking forward to combining ControlNet and LoRA with image-to-image functions to create more diverse visual effects.
Fischoff demonstrated images generated by FLUX using ControlNet contour control, and how to quickly generate new images with different styles by modifying prompt words.